Course syllabus

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(PLEASE NOTE that the course is entirely new with a lot of development work. Therefore, details should be considered as preliminary and can be expected to be updated as the course proceeds. Currently missing information will be updated at the latest when the course begins.)

Introduction

The purpose of the course is to explain how some different well-known AI-systems work, provide insight in how such systems are built, and practice to develop such systems. The course takes a broad perspective and includes related areas such as data science, algorithms and optimization as appropriate.

The main structure of the course is in the following weekly modules:

  1. AI problem solving

  2. Recommendation systems

  3. AI tools

  4. Natural language processing

  5. Diagnosis systems

  6. Games and planning

  7. Dialogue systems and question answering

  8. Your own mini-project

There may additionally be single lectures that are not connected to a particular module.

Official course plan  (here some details like learning objectives can be found) 

Contact details

Dag Wedelin (course responsible) dag at chalmers.se
Fredrik Johansson (responsible for several modules) fredrik.johansson at chalmers.se
Emil Carlsson (TA) caremil at chalmers.se
Please contact Emil for administrative questions about assignments, groups etc.)

Course representatives:

  • Davíd Freyr Björnsson
  • Tobias Karlsson
  • Akshita Pingle

Schedule

Lectures on Tuesday and Friday at 10 in varying rooms. See the TimeEdit schedule.

Supervision normally available by at least one of us  Thursday 10-12 and Tuesday 15-17 [This slot cancelled starting Feb 11] in our offices (EDIT, 6481/6447/6451) or via Skype or similar. However, you can also contact us at other times subject to availability (by phone, Skype or email, not office). Please note that different people will be responsible for supervision depending on the module.

Examination and grading

The course is examined continuously through the module submissions. Grading will be based on a qualitative assessment of each module.

The final grade is based on an overall assessment at the end of the course. If you are close to a grade boundary, you can discuss with the main teacher(s), and you will be given an opportunity to improve at the end of the course.

If you should not complete the course in time, and need to come back next year, it is in your best interest to keep copies of your solutions to enable future assessment.

Course literature

Reading instructions will be provided in connection to the modules.

Changes made since the last occasion

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Course summary:

Date Details Due